The Reticulate Package

Most direct route to Data Science :-)

- BS in Applied Science from US Coast Guard Academy; studed heavy metals in surface waters

- MS in Chemisry researching organometallic semiconductors

- MBA in Finance

- Spent most of my career in modeling hurricanes, earthquakes, terrorism and other catastrophes in the insurance industry (mostly referred to as predictive modeling)

- Also worked in banking, marketing, and insuretech

- Most corporations are sad silos

- R Markdown and reticulate power a new way forward

- Jupyter Notebook problem - (what got me to try reticulate)

- Wrapping Python in R Markdown for time series work, take advantage of key functions in R Markdown

- Writing Python in the reticulate syntax

- RNN time series model

- Bonus

- R library that allows for writing Python code in RStudio

Option A

- Write pure Python code in R Markdown to generate cleaner visuals than Jupyter Notebook

Option B

- Integrate Python and R libraries using R syntax

- Slight change to using ‘$’ in place of the “.” for Python

- Can use both Python and R libraries interchangeably

Option C

- Use the R Notebook which runs off R Markdown

- Must have a virtual environment to call use_condaenv(‘base’) does not work

- For imports make R objects: pd <- import(‘pandas’)

- Use the “$ in place of the”.", when you hover, it shows the methods available

- Need to use the print() command more, same for using Python in R Markdown; cannot just call the object

- There is no command %inline for Matplotlib

- The library PyQt5 is needed for plotting (provides support for UI and browsers)

- Sometimes you need to add plt.clf() before a plot to clear Matplotlib out

- The syntax differs from Jupyter to R Markdown, need to assign the plotting components (example that I wrote for a person’s question on Stackoverflow)

- Probably easier to do complex plots with

- Matplotlib takes more lines of code and has some requirements when working Python in R Markdown

library(reticulate)
library(ggplot2)
library(reshape2)
library(zoo)

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(knitr)

use_condaenv('pytorch')

# use this theme for any ggplots
mypal <- c('darkgreen', 'grey', 'blue', 'orange')
theme_bryan <- function () { 
  theme(axis.text.x = element_text(size = 12, color = 'darkgreen', angle = 0),
        legend.position = 'bottom',
        axis.text.y = element_text(size=12, color = 'darkgreen'),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        strip.text.x = element_text(color='white', size = 10, face = 'bold'),
        strip.background = element_rect(fill = 'darkgreen'))
}

Now Load the Python Libraries

- This makes them R objects

# import the python libraries
pd <- import('pandas')
np <- import('numpy')
os <- import('os')

Load Data

- Take advantage of index_col in Pandas

- This is executing Python in R

- Switch the ‘.’ in Python for ‘$’

# check the directory
os$getcwd()

## [1] "/Users/Bryan/Documents/Programming/useR"

os$chdir("/Users/Bryan/Documents/Programming/useR")


# get the file
# load the data create the index for dates
df = pd$read_csv('Subscriptions.csv', index_col='Date', parse_dates=TRUE)
df$index = pd$to_datetime(df$index, dayfirst=T)

print(head(df))

##            Joiners Opens Potential Email
## 2018-05-01     358    45       198  1425
## 2018-06-01     255   455      1761  1096
## 2018-07-01     206   588      2318   900
## 2018-08-01     177   654      2769   843
## 2018-09-01     113   514      2477   723
## 2018-10-01     115   606      3472   703

# get the dates from the index
Dates <- as.Date(rownames(df), format = '%Y-%m-%d')

# make a separate column
df <- cbind(df, Dates)


# melt for plotting
meltDf <- melt(df, id.var='Dates')

Simple Plot

g <- ggplot(meltDf, aes(x=Dates, y=value, col=variable)) + 
    geom_line(size = 1.5) + 
    scale_fill_manual(values = mypal, aesthetics = c("colour", "fill")) +
    theme_bryan()
g

Facet Plot

g <- ggplot(meltDf, aes(x=Dates, y=value, col=variable)) + 
    geom_line(size = 1.5) + 
    scale_fill_manual(values = mypal, aesthetics = c("colour", "fill")) +
    theme_bryan() + 
    facet_wrap(~variable, ncol = 2, scales = 'free')
g

Similar Plot (in Python)

- Requires much more code

import PyQt5
import numpy as np
import pandas as pd
import os

import matplotlib.pyplot as plt
from matplotlib.pyplot import figure

data = pd.read_csv('Subscriptions.csv',index_col='Date', parse_dates=True)

# make the nice plot

# set the figure size
fig = plt.figure(figsize = (15,10))

# the series
ax1 = fig.add_subplot(211)
ax1.plot(data.index.values, data.Opens, color = 'green', label = 'Opens')

# plot the legend for the first plot
ax1.legend(loc = 'upper right', fontsize = 14)

plt.ylabel('Opens', fontsize=16)

# Hide the top x axis
ax1.axes.get_xaxis().set_visible(False)

#######  NOW PLOT THE OTHER SERIES ON A SINGLE PLOT

# plot 212 is the MI series

# plot series
ax2 = fig.add_subplot(212)
ax2.plot(data.index.values, data.Joiners, color = 'orange', label = 'Joiners')

# plot the legend for the second plot
ax2.legend(loc = 'upper right', fontsize = 14)

# set the fontsize for the bottom plot
plt.ylabel('Joiners', fontsize=16)


plt.tight_layout()
plt.show()

Using Matplotlib

- Run R code, but create Python Objects

# import key libraries
sm <- import('statsmodels.tsa.seasonal', convert = FALSE)
mpl <- import('matplotlib')
plt <- import('matplotlib.pyplot', convert = TRUE)
pd <- import('pandas', convert = FALSE)


# get the data and keep it as a pandas object
setwd("/Users/Bryan/Documents/Programming/useR")
df <- pd$read_csv('Alcohol_Sales.csv', index_col='DATE', parse_dates=TRUE)
df$index$freq = 'MS'
df$columns <- list('Sales')

# seasonal decompose
result <- sm$seasonal_decompose(df$Sales)

# make all three plots
ax1 <- result$plot()
plt$savefig('SeasDecomp.png')

# get the seasonal component only
plt$clf()
ax2 = result$seasonal$plot(figsize = c(12,8))
plt$savefig('seasonal.png')

Seasonal Decomposition Plot

Seasonal Component only

RNN using Keras and Tensorflow

Imports For Basics

Needs PyQt5 to do plotting

# need PyQt5 for plotting
import PyQt5

# import the base libraries
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
import os
import math
from itertools import cycle

# plotting libraries
import matplotlib.pyplot as plt
import seaborn as sns


# get the datetime library for date & time calcs
from datetime import datetime, timedelta

Specialized Imports

Libraries for timeseries and deep learning

Could use the R version of Keras package with tensorflow

# import modeling tools

from statsmodels.tools.eval_measures import mse, rmse

from statsmodels.tsa.seasonal import seasonal_decompose
from pylab import rcParams

# need to scale data to 0 - 1 
from sklearn.preprocessing import MinMaxScaler

# get the deep learning components
from keras.models import Sequential

## Using TensorFlow backend.

from keras.layers import Dense
from keras.layers import LSTM

# use the preprocessing time series generator
from keras.preprocessing.sequence import TimeseriesGenerator

import random

random.seed(1000)

Get the Data

os.chdir('/Users/Bryan/Documents/Programming/useR')
os.getcwd()

## '/Users/Bryan/Documents/Programming/useR'

df = pd.read_csv('Alcohol_Sales.csv', index_col='DATE', parse_dates=True)
df.index.freq = 'MS'
df.head()

# get a view of the data

##             S4248SM144NCEN
## DATE                      
## 1992-01-01            3459
## 1992-02-01            3458
## 1992-03-01            4002
## 1992-04-01            4564
## 1992-05-01            4221

df.columns = ['Sales']
df.plot(figsize=(16,8))


from statsmodels.tsa.seasonal import seasonal_decompose
result = seasonal_decompose(df['Sales'])

rcParams['figure.figsize'] = 16,10
result.plot()

result.seasonal.plot(figsize=(12,8))


# nobs = number of observations to forecast
nobs = 12

train = df.iloc[:-nobs]
test = df.iloc[-nobs:]

len(test)

## 12

## MinMaxScaler(copy=True, feature_range=(0, 1))

## [[0.63116506]
##  [0.82502778]
##  [0.75972305]
##  [0.94939738]
##  [0.98743482]
##  [0.82135225]
##  [0.95956919]
##  [0.80049577]
##  [0.93025045]
##  [0.95247457]
##  [1.0661595 ]
##  [0.65706471]]

## <bound method Network.summary of <keras.engine.sequential.Sequential object at 0x1c3e212be0>>

## Epoch 1/25
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## Epoch 2/25
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301/301 [==============================] - 8s 27ms/step - loss: 0.0101
## Epoch 3/25
## 
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301/301 [==============================] - 8s 27ms/step - loss: 0.0080
## Epoch 4/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0076
## Epoch 5/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0061
## Epoch 6/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0055
## Epoch 7/25
## 
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## Epoch 8/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0034
## Epoch 9/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0027
## Epoch 10/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0028
## Epoch 11/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0020
## Epoch 12/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0024
## Epoch 13/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0024
## Epoch 14/25
## 
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## Epoch 15/25
## 
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## Epoch 16/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0018
## Epoch 17/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0020
## Epoch 18/25
## 
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## Epoch 19/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0021
## Epoch 20/25
## 
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## Epoch 21/25
## 
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## Epoch 22/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0014
## Epoch 23/25
## 
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301/301 [==============================] - 8s 26ms/step - loss: 0.0015
## Epoch 24/25
## 
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## Epoch 25/25
## 
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## <keras.callbacks.History object at 0x11c3efba8>

plt.clf()
plt.plot(range(len(model.history.history['loss'])), model.history.history['loss'])

first_eval_batch = scaled_train[-12:]

first_eval_batch = first_eval_batch.reshape((1,n_input, n_features))
model.predict(first_eval_batch)

# holding predictions in empty list

## array([[0.7478991]], dtype=float32)

test_predictions = []


# last n_inputs from the training set
first_eval_batch = scaled_train[-n_input:]

current_batch = first_eval_batch.reshape((1,n_input, n_features))

# how far into the future to forecast
# set to length of test set
# can change the range and go into the future
for i in range(len(test)):
    
    # one time step ahead of historical 12 points
    current_pred = model.predict(current_batch)[0]
    
    # store in prediction
    test_predictions.append(current_pred)
    
    # UPDATE current batch to include prediction
    # dropping first and adding the prediction
    current_batch = np.append(current_batch[:,1:,:], [[current_pred]], axis = 1)
    
    
    
    
print(test_predictions)
# invert the scaling 

## [array([0.7478991], dtype=float32), array([0.89395285], dtype=float32), array([0.83914036], dtype=float32), array([1.0242552], dtype=float32), array([1.0912414], dtype=float32), array([0.8292134], dtype=float32), array([0.9903636], dtype=float32), array([0.8562024], dtype=float32), array([0.9409095], dtype=float32), array([0.98989195], dtype=float32), array([1.055684], dtype=float32), array([0.658173], dtype=float32)]

true_predictions = scaler.inverse_transform(test_predictions)

test['Predictions'] = true_predictions

## /Users/Bryan/anaconda/envs/pytorch/bin/python:1: SettingWithCopyWarning: 
## A value is trying to be set on a copy of a slice from a DataFrame.
## Try using .loc[row_indexer,col_indexer] = value instead
## 
## See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

print(test)

##             Sales   Predictions
## DATE                           
## 2018-02-01  10415  11780.671747
## 2018-03-01  12683  13489.354352
## 2018-04-01  11919  12848.103020
## 2018-05-01  14138  15013.761076
## 2018-06-01  14583  15797.432667
## 2018-07-01  12640  12731.967342
## 2018-08-01  14257  14617.263731
## 2018-09-01  12396  13047.712153
## 2018-10-01  13914  14038.700298
## 2018-11-01  14174  14611.745880
## 2018-12-01  15504  15381.446770
## 2019-01-01  10718  10730.966215

test.plot(figsize=(12,8))

error = rmse(test['Sales'], test['Predictions'])
print(f'{error:.0f}', 'RMSE')

## 731 RMSE

SalesMean = test['Sales'].mean()
print(f'{SalesMean:.0f}', 'Mean of Sales')

## 13112 Mean of Sales

percent = error/SalesMean*100
print(f'{percent:.1f}', '% Error')

## 5.6 % Error

Alternate framework for deep learning

Follows a Numpy syntax

# import key libraries
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable

def describe(x):
    print("Type: {}".format(x.type()))
    print("Shape/size: {}".format(x.shape))
    print("Values: \n{}".format(x))
    
describe(torch.rand(2,3))

## Type: torch.FloatTensor
## Shape/size: torch.Size([2, 3])
## Values: 
## tensor([[0.9884, 0.9371, 0.3476],
##         [0.5175, 0.2534, 0.7121]])

More Tensor Operations

describe(torch.zeros(2,3))

## Type: torch.FloatTensor
## Shape/size: torch.Size([2, 3])
## Values: 
## tensor([[0., 0., 0.],
##         [0., 0., 0.]])

x = torch.ones(2,3)
describe(x)

## Type: torch.FloatTensor
## Shape/size: torch.Size([2, 3])
## Values: 
## tensor([[1., 1., 1.],
##         [1., 1., 1.]])

x.fill_(5)

## tensor([[5., 5., 5.],
##         [5., 5., 5.]])

describe(x)

## Type: torch.FloatTensor
## Shape/size: torch.Size([2, 3])
## Values: 
## tensor([[5., 5., 5.],
##         [5., 5., 5.]])

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